Long-term assessment of efgartigimod in patients with generalised myasthenia gravis: ADAPT+ study interim results
Bibliographic record
Abstract
Introduction In ADAPT, efgartigimod, a human IgG1 antibody Fc-fragment blocking neonatal Fc receptor, resulted in clinically meaningful improvement (CMI) in myasthenia gravis (MG)-specific measures. Patients completing ADAPT were eligible to enrol in ADAPT+ (open-label, 3-year extension study). Methods Efgartigimod, 10 mg/kg intravenous infusion administered once-weekly for 4 weeks; subsequent cycles initiated based on predefined criteria. Myasthenia Gravis Activities of Daily Living (MG-ADL) and Quantitative MG (QMG) scales were completed every cycle. Results From ADAPT 91% patients (151/167) entered ADAPT+. As of February 2021, 106 AChR-Ab+ and 33 AChR-Ab– had received ≥1 dose of efgartigimod (including 66 ADAPT placebo patients). Mean(SD) study duration: 363(114) days. The most common adverse events (AEs) in the efgartigimod-efgartigimod and placebo-efgartigimod arms: headache (15.1%/30.3%), nasopharyngitis (8.2%/13.6%), diarrhoea (6.8%/10.6%). Five deaths occurred, none considered efgartigimod-related by investigators. AEs were predominantly mild or moderate. CMI was observed in AChR-Ab+ patients during each cycle (up to 10 cycles), comparable to improvements at week 3, cycle 1 (mean[SE]: MG-ADL, –5.1[0.34]; QMG, –4.7[0.41]). Clinical improvements mirrored maximal reductions in total IgG and AChR-Abs across all cycles. Similar results observed in AChR-Ab– patients. Conclusions This suggests long-term efgartigimod was well-tolerated and efficacious, regardless of antibody status. No new safety signals were identified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".